论文标题

延迟云游戏内容的灵敏度分类

Delay Sensitivity Classification of Cloud Gaming Content

论文作者

Sabet, Saeed Shafiee, Schmidt, Steven, Zadtootaghaj, Saman, Griwodz, Carsten, Moller, Sebastian

论文摘要

Cloud Gaming是一项新兴服务,引起了人们对研究界和行业的兴趣日益增长的兴趣。尽管范式从客户对客户的执行到云流媒体游戏的转变提供了各种好处,但新服务还需要一个高度可靠且低的延迟网络,以实现令人满意的体验质量(QOE)。使用具有高潜伏期的云游戏服务会损害用户与游戏的相互作用,从而导致游戏性能下降,从而降低玩家的挫败感。但是,延迟对游戏QoE的负面影响很大程度上取决于游戏内容。在一定程度的延迟水平上,节奏缓慢的纸牌游戏通常不像射击游戏那样延迟敏感。为了获得最佳的资源分配和质量估计,对于云提供商,游戏开发人员和网络计划者来说,考虑游戏内容的影响非常重要。本文通过识别影响用户延迟感知的游戏特征来更好地理解对云游戏应用程序的延迟影响。此外,提出了一种量化这些特征的专家评估方法,以及基于决策树的延迟灵敏度分类。 14位专家对量化的评分表明,一致性的水平表明了该方法的可靠性。此外,在确定一系列实验期间,决策树的精度达到了86.6%的精度,这些延迟灵敏度类别是从大型主观输入质量评级数据集中得出的。

Cloud Gaming is an emerging service that catches growing interest in the research community as well as industry. While the paradigm shift from a game execution on clients to streaming games from the cloud offers a variety of benefits, the new services also require a highly reliable and low latency network to achieve a satisfying Quality of Experience (QoE) for its users. Using a cloud gaming service with high latency would harm the interaction of the user with the game, leading to a decrease in playing performance and thus frustration of players. However, the negative effect of delay on gaming QoE depends strongly on the game content. At a certain level of delay, a slow-paced card game is typically not as delay sensitive as a shooting game. For optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the impact of the game content. This paper contributes to a better understanding of the delay impact on QoE for cloud gaming applications by identifying game characteristics influencing the delay perception of users. In addition, an expert evaluation methodology to quantify these characteristics, as well as a delay sensitivity classification based on a decision tree is presented. The ratings of 14 experts for the quantification indicated an excellent level of agreement which demonstrates the reliability of the proposed method. Additionally, the decision tree reached an accuracy of 86.6 % on determining the delay sensitivity classes which were derived from a large dataset of subjective input quality ratings during a series of experiments.

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